Bioinformatics for Novel Corona Virus-SARS-CoV-2/COVID-19
August 22, 2020
The very first bioinformatic breakthrough in this outbreak was the detailing of the new coronavirus’ biological sequence. The genome of COVID-19 and the crystal structure of its protease enzyme have already been sequenced and elucidated.
Table of Contents
Phylogeny
phylogenetic trees usually reveal individual shared mutations. Using this new approach, the team made use of “genome‐wide co‐developing functionalities,” developing a more flexible view of the different variations of the RNA viruses over time and sequential multiplications.
Naming of SARS-CoV-2
Viruses are named based on their genetic structure to facilitate the development of diagnostic tests, vaccines and medicines. Virologists and the wider scientific community do this work, so viruses are named by the International Committee on Taxonomy of Viruses (ICTV). Official names have been announced for the virus responsible for COVID-19 (previously known as “2019 novel coronavirus”) and the disease it causes.
CTV announced “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)” as the name of the new virus on 11 February 2020. This name was chosen because the virus is genetically related to the coronavirus responsible for the SARS outbreak of 2003. While related, the two viruses are different.
Phylogenetic tree of SARS-CoV-2 and other related coronaviruses. SARS-CoV-2 isolated from patients in Wuhan (pink) and its related coronavirus Pangolin-CoV (red) and Bat CoV RaTG13 (green). Figure adapted from Zhange et al. (2020).
Identification of host
It is revealed that the genome of SARS-CoV-2 is 96% identical to a bat coronavirus (RaTG13), suggesting that they share the same host – bats. Moreover, spike (S) proteins are important for the virus to bind to a human cell receptor and mediates cell entry.
It is one of the most important functional proteins of SARS-CoV-2. There is 93.1% S protein nucleotide similarity between that of SARS-CoV-2 and RaTG13, thus further showing their relatedness.
Bioinformatics role in diagnostic tool for COVID-19
The sequence is a starting point in developing drugs, diagnostic tools and a vaccine for the dangerous virus. The effort to find the genes that are responsible for viral replication and the protein responsible for the host cell attachment have benefitted from this sequencing.
Bioinformatics for Drug repurposing
Currently there are no medicines or vaccines that have been claimed to be useful in the prevention or treatment of COVID-19. Due to the absence of effective treatment and public health emergency, the COVID-19 pandemic is alarming the entire globe. International health care authorities are doing their efforts to provide quarantine and quick diagnosis for COVID-19 patients along with research to find an effective treatment that can control and prevent the present dangerous consequences of the disease.
Drug repurposing (also called drug repositioning, reprofiling or re‑tasking) is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. This strategy offers various advantages over developing an entirely new drug for a given indication.
First, and perhaps most importantly, the risk of failure is lower; because the repurposed drug has already been found to be sufficiently safe in preclinical models and humans if early-stage trials have been completed, it is less likely to fail at least from a safety point of view in subsequent efficacy trials. Second, the time frame for drug development can be reduced, because most of the preclinical testing, safety assessment and, in some cases, formulation development
will already have been completed. Third, less investment is needed, although this will vary greatly depending on the stage and process of development of the repurposing candidate. The regulatory and phase III costs may remain more or less the same for a repurposed drug as for a new drug in the same indication, but there could still be substantial savings in preclinical and phase I and II costs.
Together, these advantages have the potential to result in a less risky and more rapid return on investment in the development of repurposed drugs, with lower average associated costs once failures have been accounted for (indeed, the costs of bringing a repurposed drug to market have been estimated to be US$300 million on average, compared with an estimated ~$2–3 billion for a new chemical entity). Finally, repurposed drugs may reveal new targets and pathways that can be further exploited.
Bioinformatics methods for drug repurposing
Computational approaches are largely data-driven; they involve systematic analysis of data of any type (such as gene expression, chemical structure, genotype or proteomic data or electronic health records (EHRs)), which can then lead to the formulation of repurposing hypotheses.
1. Signature matching
Signature matching is based on the comparison of the unique characteristics or ‘signature’ of a drug against that of another drug, disease or clinical phenotype.
2. Computational molecular docking
Molecular docking is a structure-based computational strategy to predict binding site complementarity between the ligand (for example, a drug) and the target (for example, a receptor.
3. Genome-wide association studies
GWAS aim to identify genetic variants associated with common diseases and thereby provide
insights into the biology of diseases; the data obtained may also help identify novel targets, some of which could be shared between diseases treated by drugs and disease phenotypes studied by GWAS and thereby lead to repositioning of drugs.
4. Pathway or network mapping
Pathway-based or network-based approaches have been widely used to identify drugs or drug targets that may have potential in repurposing,
5. Retrospective clinical analysis: use of electronic health records (EHR)
Retrospective clinical data can be obtained from various sources, including EHRs, post-marketing surveillance data and clinical trial data. EHRs contain an enormous amount of data on patient outcomes, both structured and unstructured. The diagnostic and patho‑physiological data, including the results of laboratory tests as well as drug prescribing data, are more structured; however, EHRs also contain considerable amounts of unstructured information, such as clinical descriptions of patient symptoms and signs (which are important in defining disease phenotype) and imaging data. This wealth of data present in EHRs could be used as a source for identifying signals for drug repurposing.
6. Novel sources of data for drug repurposing
Immortalized human cancer cell lines (CCLs) have been used in high-throughput drug screens against hundreds of compounds (both approved and experimental) to test their effect on cell viability.
Several existing antiviral drugs, previously developed or used as treatments for SARS, MERS, HIV, and malaria are being investigated as COVID-19 treatments and some of which are being used in clinical trials.However, a lot of work needs to be done to achieve a better treatment outcome and it will take some time to establish the complete safety and efficacy of the trial drugs. Therefore, further clinical studies and large randomized control studies are needed for the better treatment option and safety of COVID-19 patients.
Bioinformatics for novel coronavirus vaccine development
Vaccines are the pharmaceutical products that offer the best cost‐benefit ratio in the prevention or treatment of diseases. In that a vaccine is a pharmaceutical product, vaccine development and production are costly and it takes years for this to be accomplished. Several approaches have been applied to reduce the times and costs of vaccine development, mainly focusing on the selection of appropriate antigens or antigenic structures, carriers, and adjuvants. One of these approaches is the incorporation of bioinformatics methods and analyses into vaccine development.
Novel approaches leverage high throughput sequencing and bioinformatics to identify promising antigens, molecular adjuvants to target specific innate cellular receptors and drive desired inflammatory responses, advanced DNA, RNA, and protein delivery systems, and are beginning to exploit detailed molecular insights gained from studying protective immune responses generated in the context of natural infection, and a greater understanding of naïve immune repertoires. the reverse vacinology strategy utilizes genome informatics as opposed to traditional biochemical and genetic tools to identify antigen targets with promising characteristics such as surface expression, secretion, and/or high conservation, which can then be empirically tested and screened as candidate immunogens. Similarly, proteomic tools have been utilized to identify surface antigens at high throughput by coupling proteolytic digestion of surface proteins with mass spectrometric protein fragment detection
The vaccine development effort is about to start. As worldwide lead compounds repositories such as pubchem (a database of chemical molecules) and drug bank are available, scientists can leverage machine-learning-based computational methods to alter drug candidates swiftly. This can be done by modifying the chemical structure of the existing coronavirus drugs with computational chemistry tools.
References
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